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1 |
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|a Martens, David
|c (Professor of data science),
|e author.
|
| 245 |
1 |
0 |
|a Data science ethics
|b concepts, techniques and cautionary tales
|c David Martens
|
| 264 |
|
1 |
|a Oxford, United Kingdom
|b Oxford University Press
|c [2022]
|
| 300 |
|
|
|a xii, 255 pages :
|b illustrations (some color), 1 colour map ;
|c 24 cm
|
| 336 |
|
|
|a text
|2 rdacontent
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| 336 |
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|a still image
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|a unmediated
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|a volume
|2 rdacarrier
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| 504 |
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|
|a Includes bibliographical references and index.
|
| 520 |
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|
|a Data science ethics is all about what is right and wrong when conducting data science. Data science has so far been primarily used for positive outcomes for businesses and society. However, just as with any technology, data science has also come with some negative consequences: an increase of privacy invasion, data-driven discrimination against sensitive groups, and decision making by complex models without explanations. While data scientists and business managers are not inherently unethical, they are not trained to weigh the ethical considerations that come from their work - Data Science Ethics addresses this increasingly significant gap and highlights different concepts and techniques that aid understanding, ranging from k-anonymity and differential privacy to homomorphic encryption and zero-knowledge proofs to address privacy concerns, techniques to remove discrimination against sensitive groups, and various explainable AI techniques. Real-life cautionary tales further illustrate the importance and potential impact of data science ethics, including tales of racist bots, search censoring, government backdoors, and face recognition. The book is punctuated with structured exercises that provide hypothetical scenarios and ethical dilemmas for reflection that teach readers how to balance the ethical concerns and the utility of data. --
|c Provided by publisher.
|
| 500 |
|
|
|a Formerly CIP.
|5 Uk
|
| 650 |
|
0 |
|a Big data
|x Moral and ethical aspects.
|
| 650 |
|
0 |
|a Data mining
|x Moral and ethical aspects.
|
| 650 |
|
6 |
|a Données volumineuses
|x Aspect moral.
|
| 650 |
|
6 |
|a Exploration de données (Informatique)
|x Aspect moral.
|
| 650 |
|
7 |
|a MATHEMATICS / General.
|2 bisacsh
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| 980 |
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|a 020423801
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SOLR
| _version_ |
1778756513461960704 |
| access_facet |
Local Holdings |
| author |
Martens, David (Professor of data science) |
| author_facet |
Martens, David (Professor of data science) |
| author_role |
aut |
| author_sort |
Martens, David (Professor of data science) |
| author_variant |
d m dm |
| building |
Library A |
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Q - Science |
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QA76 |
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QA76.9.B45 M36 2022 |
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QA76.9.B45 M36 2022 |
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QA 276.9 B45 M36 42022 |
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QA - Mathematics |
| collection |
sid-180-col-bnbfidbbi |
| contents |
Data science ethics is all about what is right and wrong when conducting data science. Data science has so far been primarily used for positive outcomes for businesses and society. However, just as with any technology, data science has also come with some negative consequences: an increase of privacy invasion, data-driven discrimination against sensitive groups, and decision making by complex models without explanations. While data scientists and business managers are not inherently unethical, they are not trained to weigh the ethical considerations that come from their work - Data Science Ethics addresses this increasingly significant gap and highlights different concepts and techniques that aid understanding, ranging from k-anonymity and differential privacy to homomorphic encryption and zero-knowledge proofs to address privacy concerns, techniques to remove discrimination against sensitive groups, and various explainable AI techniques. Real-life cautionary tales further illustrate the importance and potential impact of data science ethics, including tales of racist bots, search censoring, government backdoors, and face recognition. The book is punctuated with structured exercises that provide hypothetical scenarios and ethical dilemmas for reflection that teach readers how to balance the ethical concerns and the utility of data. -- |
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(OCoLC)on1273469063 |
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005.7 |
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000 - Computer science, information & general works |
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005 - Computer programming, programs & data |
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005.7 |
| dewey-search |
005.7 |
| dewey-sort |
15.7 |
| dewey-tens |
000 - Computer science, knowledge & systems |
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Local |
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Informatik, Mathematik |
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science-computerscience |
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Formerly CIP. |
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Book |
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Book, E-Book |
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Book, E-Book |
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Book, E-Book |
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Buch |
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Book, E-Book |
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Book |
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not assigned |
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not assigned |
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180-020423801 |
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Illustrated |
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Oxford, United Kingdom, Oxford University Press, [2022] |
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Oxford, United Kingdom Oxford University Press [2022] |
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FID-BBI-DE-23 |
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9780192847263, 9780192847270 |
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FID-BBI-DE-23 |
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English |
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2023-10-03T17:33:21.712Z |
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2021946685 |
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martens2022datascienceethicsconceptstechniquesandcautionarytales |
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British National Bibliography |
| oclc_num |
1273469063 |
| physical |
xii, 255 pages; illustrations (some color), 1 colour map; 24 cm |
| publishDate |
[2022] |
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2022 |
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Oxford, United Kingdom |
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Oxford University Press |
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020423801 |
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| spelling |
Martens, David (Professor of data science), author., Data science ethics concepts, techniques and cautionary tales David Martens, Oxford, United Kingdom Oxford University Press [2022], xii, 255 pages : illustrations (some color), 1 colour map ; 24 cm, text rdacontent, still image rdacontent, unmediated rdamedia, volume rdacarrier, Includes bibliographical references and index., Data science ethics is all about what is right and wrong when conducting data science. Data science has so far been primarily used for positive outcomes for businesses and society. However, just as with any technology, data science has also come with some negative consequences: an increase of privacy invasion, data-driven discrimination against sensitive groups, and decision making by complex models without explanations. While data scientists and business managers are not inherently unethical, they are not trained to weigh the ethical considerations that come from their work - Data Science Ethics addresses this increasingly significant gap and highlights different concepts and techniques that aid understanding, ranging from k-anonymity and differential privacy to homomorphic encryption and zero-knowledge proofs to address privacy concerns, techniques to remove discrimination against sensitive groups, and various explainable AI techniques. Real-life cautionary tales further illustrate the importance and potential impact of data science ethics, including tales of racist bots, search censoring, government backdoors, and face recognition. The book is punctuated with structured exercises that provide hypothetical scenarios and ethical dilemmas for reflection that teach readers how to balance the ethical concerns and the utility of data. -- Provided by publisher., Formerly CIP. Uk, Big data Moral and ethical aspects., Data mining Moral and ethical aspects., Données volumineuses Aspect moral., Exploration de données (Informatique) Aspect moral., MATHEMATICS / General. bisacsh |
| spellingShingle |
Martens, David (Professor of data science), Data science ethics: concepts, techniques and cautionary tales, Data science ethics is all about what is right and wrong when conducting data science. Data science has so far been primarily used for positive outcomes for businesses and society. However, just as with any technology, data science has also come with some negative consequences: an increase of privacy invasion, data-driven discrimination against sensitive groups, and decision making by complex models without explanations. While data scientists and business managers are not inherently unethical, they are not trained to weigh the ethical considerations that come from their work - Data Science Ethics addresses this increasingly significant gap and highlights different concepts and techniques that aid understanding, ranging from k-anonymity and differential privacy to homomorphic encryption and zero-knowledge proofs to address privacy concerns, techniques to remove discrimination against sensitive groups, and various explainable AI techniques. Real-life cautionary tales further illustrate the importance and potential impact of data science ethics, including tales of racist bots, search censoring, government backdoors, and face recognition. The book is punctuated with structured exercises that provide hypothetical scenarios and ethical dilemmas for reflection that teach readers how to balance the ethical concerns and the utility of data. --, Big data Moral and ethical aspects., Data mining Moral and ethical aspects., Données volumineuses Aspect moral., Exploration de données (Informatique) Aspect moral., MATHEMATICS / General. |
| title |
Data science ethics: concepts, techniques and cautionary tales |
| title_auth |
Data science ethics concepts, techniques and cautionary tales |
| title_full |
Data science ethics concepts, techniques and cautionary tales David Martens |
| title_fullStr |
Data science ethics concepts, techniques and cautionary tales David Martens |
| title_full_unstemmed |
Data science ethics concepts, techniques and cautionary tales David Martens |
| title_short |
Data science ethics |
| title_sort |
data science ethics concepts techniques and cautionary tales |
| title_sub |
concepts, techniques and cautionary tales |
| topic |
Big data Moral and ethical aspects., Data mining Moral and ethical aspects., Données volumineuses Aspect moral., Exploration de données (Informatique) Aspect moral., MATHEMATICS / General. |
| topic_facet |
Big data, Data mining, Données volumineuses, Exploration de données (Informatique), MATHEMATICS / General., Moral and ethical aspects., Aspect moral. |